Modelling heterogeneous location habits in human populations for location prediction under data sparsity

In recent years, researchers have sought to capture the daily life location behaviour of groups of people for exploratory, inference, and predictive purposes. However, development of such approaches has been limited by the requirement of personal semantic labels for locations or social/spatial overlap between individuals in the group. To address this shortcoming, we present a Bayesian model of mobility in populations (i.e., groups without spatial or social interconnections) that is not subject to any of these requirements. The model intelligently shares temporal parameters between people, but keeps the spatial parameters specific to individuals. To illustrate the advantages of population modelling, we apply our model to the difficult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and find a factor of 2.4 improvement in location prediction performance against a state-of-the-art model when training on only 20 hours of observations.